Assessment of the effects of dredging on metal levels in port waters using DGT passive samplers and spot sampling.

Aiming at assessing the effect of dredging activities on the levels of metals in Bilbao Port (northern Spain), dissolved and labile metal concentrations in the water were concurrently measured, before, during, and after dredging activities by spot sampling and Diffusive Gradients in Thin-films (DGTs) passive samplers, respectively. Most of the dissolved metal results were below the quantification limits (Cd, <0.06-0.26 µg/L; Co, <5 µg/L; Cu, <5-15 µg/L; Fe, <10-48 µg/L; Mn, <10-22 µg/L; Ni, <2.6-7 µg/L; Pb, <0.39-0.8 µg/L; Zn, <9-24 µg/L). In contrast, DGT results for all sampling times and stations were obtained (Cd, 0.02-0.12 µg/L; Co, 0.08-0.15 µg/L; Cu, 0.5-2.8 µg/L; Fe, 1.0-3.6 µg/L; Mn, 4.7-23.5 µg/L; Ni, 0.5-0.9 µg/L; Pb, 0.15-0.28 µg/L; Zn, 2.6-7.2 µg/L), enabling to determine those metals affected by dredging. Only labile-Pb concentration surpassed momentarily the DGT-Environmental Quality Standard, enabling to rule out biological effects on biota. DGTs are a promising technique for facilitating decision-making during dredging operations.

Urban spatial structure and air quality in the United States: Evidence from a longitudinal approach.

Previous studies on the relationship between urban form and air quality: (1) report mixed results among specific aspects of urban spatial structure (e.g., urban expansion, form, or shape) and (2) use primarily cross-sectional approaches with a single year of data. This study takes advantage of a multi-decade, longitudinal approach to investigate the impact of urban spatial structure on population-weighted concentrations of PM2.5 and NO2. Based on fixed-effect regression models for 481 urban areas in the United States spanning from 1990 to 2015, we found significant associations between various aspects of urban spatial structure and air quality after controlling for meteorological and socio-economic factors. Our results show that population density, compact urban form, circularity, and green space are associated with lower concentrations. Conversely, higher rates of urban expansion, industrial area, and polycentricity are associated with higher concentrations. For large cities (total population: 180,262,404), we found that increasing key factors from each urban spatial structure category (i.e., greenness, population density, compactness, circularity) by a modest 10% results in 10,387 (12,376) fewer deaths for PM2.5 (NO2). We recommend that policymakers adopt comprehensive strategies to increase population density, compactness, and green spaces while slowing urban expansion to reduce the health burden of air quality in US cities.

Designing Energy-Efficient Buildings in Urban Centers through Machine Learning and Enhanced Clean Water Management.

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

A systematic review of whether Health Impact Assessment frameworks support best practice principles.

OBJECTIVES: Health Impact Assessment (HIA) is an evidence-based approach to assess the likely public health impacts of a policy or plan in any sector. Several HIA frameworks are available to guide practitioners doing a HIA. This systematic review sought to determine whether these support practitioners to meet best practice principles defined by the International Association for Impact Assessment. STUDY DESIGN: This was a systematic review. METHODS: Three complementary search strategies were used to identify frameworks in June 2022. We used three databases to find completed HIAs published in the last five years and hand-searched their reference lists for frameworks. We also searched 23 HIA repositories using Google's Advanced function and contacted HIA practitioners via two international mailing lists. We used a bespoke quality appraisal tool to assess frameworks against the principles. RESULTS: The search identified 24 HIA frameworks. None of the frameworks achieved a 'good' rating for all best practice principles. Many identified the principles but did not provide guidance on how to meet them at all HIA steps. The highest number of frameworks were rated 'good' for ethical use of evidence and comprehensive approach to health (n = 15). Eight frameworks were rated as 'good' for participation, and two for equity. The highest number of frameworks rated 'poor' for sustainability (n = 11). CONCLUSIONS: There is marked variation in the degree to which HIA frameworks support the best practice principles. HIA practitioners could select elements from different frameworks for practical guidance to meet all the best practice principles.

Re-assessment of schistosomiasis in nine endemic districts with cluster sampling in Sierra Leone.

Background: Baseline mapping showed that schistosomiasis was highly/moderately endemic in nine districts in Sierra Leone. Mass drug administration (MDA) with praziquantel started in 2009, and after multiple rounds of treatment, an impact assessment was conducted in 2016 followed by a second re-assessment in 2022 using cluster sampling to provide more granular data for refining chiefdom (sub-district) treatment strategies. Methods: On average, 20 rural villages were systematically selected per district by probability proportional to population size across the nine districts. Surveys were conducted in schools, and 24 school children aged between 5 and 14 years were randomly selected, with an equal number of boys and girls. One stool sample and one urine sample were collected per child. Two Kato-Katz slides were examined per stool for Schistosoma mansoni infection. Hemastix strips were used as a proxy for S. haematobium infection with urine filtration used for egg counts on hematuria-positive samples. Results: In total, 4,736 stool samples and 4,618 urine samples were examined across 200 schools in 125 chiefdoms. Overall, the prevalence of S. mansoni was 16.3% (95% CI: 15.3-17.4%), while the overall prevalence of S. haematobium was 2.0% (95% CI: 1.6-2.4%) by hematuria. The prevalence of heavy infections for S. mansoni and S. haematobium was 1.5% (95% CI: 1.1-1.9%) and 0.02% (95% CI: 0.0-0.14%), respectively. Among 125 chiefdoms surveyed, the overall schistosomiasis prevalence was <10% in 65 chiefdoms, 10-49.9% in 47 chiefdoms, and ≥ 50% in 13 chiefdoms. There was a mixed relationship between schistosomiasis in school children and WASH access in schools. Conclusion: Sierra Leone has made significant progress in reducing schistosomiasis prevalence across the country after a decade of MDA intervention. However, high prevalence remains in some hotspot chiefdoms. The next steps are for the national program to investigate and address any potential issues such as low coverage or poor knowledge of schistosomiasis risk behaviors and, where appropriate, consider broadening to community-wide treatment in hotspot chiefdoms or communities.

An evaluation of air quality impact prediction performance undertaken as part of environmental impact assessment (EIA) in India.

Effective implementation of the Environmental Impact Assessment (EIA) is recognised as a global issue, in particular the impact prediction stage, which is the 'core' of EIA. Consisting of four stages: impact identification, impact assessment, significance evaluation, and mitigation measures on the possible environmental repercussions of project developmental activities, the efficacy of impact prediction can define the quality of the EIA process, which will better align environmental decision-making to sustainable development. The weakness of impact prediction in EIA demands more study to enhance practice. Although this is widely explored in the context of developed countries such as the UK, it is particularly concerning in India. A specialised review package built from several sources is utilised to assess the efficacy of air quality impact prediction, based on Lee & Colley (1991). 20 EIA reports of Category A (mega-scale projects causing significant environmental impacts) are reviewed. This study's evaluation indicates that significance evaluation and mitigation actions are the weakest phases and a major concern while assessing air quality studies conducted as a part of EIA. Recommendations to improve the process include prioritising the cumulative impact assessment within the regulatory framework, enhancing capacity building, embedding public participation and instilling accountability among stakeholders, which can be adopted globally. Additional recommendations specifically for India are revising the National Ambient Air Quality Standards (NAAQS), restructuring the EIA review mechanism by EAC and improving mitigation measures by adopting GIS and remote sensing technologies.

Evaluating the effectiveness of mitigation measures in environmental impact assessments: A comprehensive review of development projects in Korea.

Rapid urbanization and development projects in Korea have posed significant threats to biodiversity; thus, effective mitigation measures are required to preserve natural habitats. Nevertheless, the factors underlying variations in mitigation measure effectiveness according to the disturbance level and surrounding environmental conditions have not been clarified. This study evaluated the effectiveness of mitigation measures implemented in environmental impact assessments (EIAs) of development projects in Korea, with a focus on their effectiveness with respect to the disturbance level and surrounding environmental conditions. A review of 288 EIA reports from selected projects that implemented all 10 mitigation measures classified according to the Wildlife Conservation Comprehensive Plan was conducted. Using the biodiversity tipping point framework, the effects of mitigation measures on biodiversity were categorized into four levels and analyzed. Analysis of variance and redundancy analysis were then performed to discern the variance in mitigation measure effectiveness in terms of the disturbance level, surrounding environment, and species. The results revealed significant variations in the effectiveness of mitigation measures depending on the surrounding environment and disturbance level. Linear projects exhibited a clear impact on various species as the disturbance level increased, whereas area-based projects did not exhibit such pronounced effects. All species demonstrated a negative relationship with development duration, development area, and distance from urban centers. Notably, avian and amphibian species showed a strong negative correlation with the digital elevation model while reptiles and mammals exhibited a strong positive relationship with pre-development biodiversity and distance from protected areas, respectively. Mitigation measures play a key role in alleviating the adverse effects of development projects; therefore, our findings indicate the need for spatially tailored mitigation plans to augment their effectiveness.

Gas engine CCHP system optimization: An energy, exergy, economic, and environment analysis and optimization based on developed northern goshawk optimization algorithm.

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in C O 2 emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Better accounting for long-term health effects in economic assessments: an illustration for air pollution in the Canton of Geneva.

OBJECTIVES: We propose a general framework for estimating long-term health and economic effects that takes into account four time-related aspects. We apply it to a reduction in exposure to air pollution in the Canton of Geneva. STUDY DESIGN: Methodological developments on the evaluation of long-term economic and health benefits, with an empirical illustration. METHODS: We propose a unified framework-the comprehensive impact assessment (CIA)-to assess the long-term effects of morbidity and mortality in health and economic terms. This framework takes full account of four time-related issues: cessation lag, policy/technical implementation timeframe, discounting and time horizon. We compare its results with those obtained from standard quantitative health impact assessment (QHIA) in an empirical illustration involving air pollution reduction in the canton of Geneva. RESULTS: We find that by neglecting time issues, the QHIA estimates greater health and economic benefits than the CIA. The overestimation is about 50% under reasonable assumptions and increases ceteris paribus with the magnitude of the cessation lag and the discount factor. It decreases both with the time horizon and with the implementation timeframe. CONCLUSION: A proper evaluation of long-term health and economic effects is an important issue when they are to be used in cost-benefit analyses, particularly for mortality, which often represents the largest fraction. We recommend using the CIA to calculate more accurate values.

Funding the pandemic response for Indigenous Peoples: an equity-based analysis of COVID-19 using a Health Equity Impact Assessment (HEIA) Indigenous lens tool.

This study examines the allocation of COVID-19 funding for Indigenous Peoples in Canada, Australia, New Zealand, and the United States during the pandemic's first wave. Indigenous communities, already facing health disparities, systemic discrimination, and historical forces of colonisation, found themselves further vulnerable to the virus. Analysing the funding policies of these countries, we employed a Health Equity Impact Assessment (HEIA) tool and an Indigenous Lens Tool supplement to evaluate potential impacts. Our results identify three major funding equity issues: unique health and service needs, socioeconomic disparities, and limited access to community and culturally safe health services. Despite efforts for equitable funding, a lack of meaningful consultation led to shortcomings, as seen in Canada's state of emergency declaration and legal disputes in the United States. New Zealand stood out for integrating Maori perspectives, showcasing the importance of consultation. The study calls for a reconciliation-minded path, aligning with Truth and Reconciliation principles, the UN Declaration on the Rights of Indigenous Peoples, and evolving government support. The paper concludes that co-creating equitable funding policies grounded in Indigenous knowledge requires partnership, meaningful consultation, and organisational cultural humility. Even in emergencies, these measures ensure responsiveness and respect for Indigenous self-determination.

Effects of ambient air pollution from shipping on mortality: A systematic review.

BACKGROUND: Shipping contributes to air pollution causing adverse health effects. We conducted for the first time a systematic review on the health and economic impacts of ambient air pollution from shipping emissions. METHODS: We performed a systematic search in PubMed, Web of Science, EBSCO (Medline), and Scopus of all time up to December 2023. We then inter-compared semi-quantitatively the results of the included eligible studies. RESULTS: We identified 23 eligible studies, 22 applying health impact assessment, and 1 using epidemiological methods. These studies used different methods for the evaluation of emissions, dispersion, and exposure, and for the exposure-mortality risk functions for exposure to shipping emissions for 1-2 years. The estimated excess global all-cause mortality from six studies ranged between 1 and 5 deaths per 100,000 person-years. However, the heterogeneity of the methods and critical gaps in the reporting seriously limited the synthesis of the evidence on health and economic effects of shipping emissions. Sufficient spatial and temporal resolutions in both dispersion and exposure modeling, as well as presentation of uncertainties is needed. Health impact assessment should present the results with all the main risk functions and population attributable risks, and the magnitude of the effect should be expressed in excess number per a given person-time or per population size. Economic effects should also cover work productivity, mental well-being, and cognitive functions. CONCLUSIONS: We recommend that future studies should properly evaluate and report the uncertainty ranges and the confidence limits of the results. Rigorous studies are needed on multipollutant exposures, exposures from various source categories, and exposures attributed to various particulate matter measures. Studies should report the health impact measures in a format that facilitates straightforward inter-study comparisons. Further research should also specifically report the used grid spacings and resolutions and evaluate whether these are optimal for the task.

A review on advancements in atmospheric microplastics research: The pivotal role of machine learning.

Microplastics (MPs), recognized as emerging pollutants, pose significant potential impacts on the environment and human health. The investigation into atmospheric MPs is nascent due to the absence of effective characterization methods, leaving their concentration, distribution, sources, and impacts on human health largely undefined with evidence still emerging. This review compiles the latest literature on the sources, distribution, environmental behaviors, and toxicological effects of atmospheric MPs. It delves into the methodologies for source identification, distribution patterns, and the contemporary approaches to assess the toxicological effects of atmospheric MPs. Significantly, this review emphasizes the role of Machine Learning (ML) and Artificial Intelligence (AI) technologies as novel and promising tools in enhancing the precision and depth of research into atmospheric MPs, including but not limited to the spatiotemporal dynamics, source apportionment, and potential health impacts of atmospheric MPs. The integration of these advanced technologies facilitates a more nuanced understanding of MPs' behavior and effects, marking a pivotal advancement in the field. This review aims to deliver an in-depth view of atmospheric MPs, enhancing knowledge and awareness of their environmental and human health impacts. It calls upon scholars to focus on the research of atmospheric MPs based on new technologies of ML and AI, improving the database as well as offering fresh perspectives on this critical issue.

The carbon footprint of a Dutch academic hospital-using a hybrid assessment method to identify driving activities and departments.

Background: The healthcare sector is responsible for 7% of greenhouse gas (GHG) emissions in the Netherlands. However, this is not well understood on an organizational level. This research aimed to assess the carbon footprint of the Erasmus University Medical Center to identify the driving activities and sources. Methods: A hybrid approach was used, combining a life cycle impact assessment and expenditure-based method, to quantify the hospital's carbon footprint for 2021, according to scope 1 (direct emissions), 2 (indirect emissions from purchased energy), and 3 (rest of indirect emissions) of the GHG Protocol. Results were disaggregated by categories of purchased goods and services, medicines, specific product groups, and hospital departments. Results: The hospital emitted 209.5 kilotons of CO2-equivalent, with scope 3 (72.1%) as largest contributor, followed by scope 2 (23.1%) and scope 1 (4.8%). Scope 1 was primarily determined by stationary combustion and scope 2 by purchased electricity. Scope 3 was driven by purchased goods and services, of which medicines accounted for 41.6%. Other important categories were medical products, lab materials, prostheses and implants, and construction investment. Primary contributing departments were Pediatrics, Real Estate, Neurology, Hematology, and Information & Technology. Conclusion: This is the first hybrid analysis of the environmental impact of an academic hospital across all its activities and departments. It became evident that the footprint is mainly determined by the upstream effects in external supply chains. This research underlines the importance of carbon footprinting on an organizational level, to guide future sustainability strategies.

Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds.

Anthropogenic disturbance of wildlife is increasing globally. Generalizing impacts of disturbance to novel situations is challenging, as the tolerance of animals to human activities varies with disturbance frequency (e.g. due to habituation). Few studies have quantified frequency-dependent tolerance, let alone determined how it affects predictions of disturbance impacts when these are extrapolated over large areas. In a comparative study across a gradient of air traffic intensities, we show that birds nearly always fled (80%) if aircraft were rare, while birds rarely responded (7%) if traffic was frequent. When extrapolating site-specific responses to an entire region, accounting for frequency-dependent tolerance dramatically alters the predicted costs of disturbance: the disturbance map homogenizes with fewer hotspots. Quantifying frequency-dependent tolerance has proven challenging, but we propose that (i) ignoring it causes extrapolations of disturbance impacts from single sites to be unreliable, and (ii) it can reconcile published idiosyncratic species- or source-specific disturbance responses.

Development of web-based software for environmental impact assessment focusing on road traffic noise.

In the Republic of Korea, Environmental Impact Assessment (EIAs) precedes development projects to predict and analyze potential environmental effects. Generally, EIA noise evaluations utilize 2D noise prediction equations and correction coefficients. This method, however, offers only a sectional noise evaluation and has limitations in complex environments with diverse noise sources. Moreover, the determination of various variables during the EIA process based on subjective human judgment raises concerns about the reliability of the results. Thus, this study aims to develop software accessible via a web environment for user-friendly EIA noise evaluations. This software supports integrated data management and generates a 3D noise prediction model for more precise and realistic analysis of noise impacts, specifically focusing on road-traffic noise at this stage of development. The 3D noise prediction model and noise map generated by the developed software have been validated against through comparison with the results of onsite noise measurements and commercial EIA software, SoundPLAN. This validation aimed to assess the practical utility of the application.

Regional impact assessment of air quality improvement: The air quality lifecourse assessment tool (AQ-LAT) for the West Midlands combined authority (WMCA) area.

Poor air quality is the largest environmental health risk in England. In the West Midlands, UK, ∼2.9 million people are affected by air pollution with an average loss in life expectancy of up to 6 months. The 2021 Environment Act established a legal framework for local authorities in England to develop regional air quality plans, generating a policy need for predictive environmental impact assessment tools. In this context, we developed a novel Air Quality Lifecourse Assessment Tool (AQ-LAT) to estimate electoral ward-level impacts of PM2.5 and NO2 exposure on outcomes of interest to local authorities, namely morbidity (asthma, coronary heart disease (CHD), stroke, lung cancer), mortality, and associated healthcare costs. We apply the Tool to assess the health economic burden of air pollutant exposure and estimate benefits that would be generated by meeting WHO 2021 Global Air Quality Guidelines (AQGs) (annual average concentrations) for NO2 (10 µg/m3) and PM2.5 (5 µg/m3) in the West Midlands Combined Authority Area. All West Midlands residents live in areas which exceed WHO AQGs, with 2070 deaths, 2070 asthma diagnoses, 770 CHD diagnoses, 170 lung cancers and 650 strokes attributable to air pollution exposure annually. Reducing PM2.5 and NO2 concentrations to WHO AQGs would save 10,700 lives reducing regional mortality by 1.8%, gaining 92,000 quality-adjusted life years (QALYs), and preventing 20,500 asthma, 7400 CHD, 1400 lung cancer, and 5700 stroke diagnoses, with economic benefits of £3.2 billion over 20 years. Significantly, we estimate 30% of QALY gains relate to reduced disease burden. The AQ-LAT has major potential to be replicated across local authorities in England and applied to inform regional investment decisions.

LED streetlight characteristics alter the functional composition of ground-dwelling invertebrates.

Artificial Light at Night (ALAN) has been identified as a primary driver of environmental change in the 21st century with key impacts on ecosystems. At the same time, developments of LED lighting systems with adjustable parameters-such as color temperature and light intensity-may provide an opportunity to mitigate the negative effects of ALAN. To test the potential effects of LED properties, we conducted a comprehensive field study over two summers at three forest sites in Switzerland. We investigated the impact of three key attributes of LED lights (color temperature, brightness, and luminaire shape) on the abundance and community structure of ground-dwelling invertebrate functional groups (predators, omnivores, and detritivores). We found a significantly increased nocturnal attraction of omnivores (+275%) and predators (+70%), but not detritivores, to ALAN, altering arthropod community composition and trophic interactions in forests. LED color temperature and luminaire shape showed minimal effects on all three functional groups, while reducing light level from 100% to 50% attracted fewer individuals in all groups with a significant effect in omnivores (-57%). In addition, we observed significant interactions of color temperatures and luminaire shapes with light intensity, with a decrease in numbers when dimming the light to 50% intensity combined with a color temperature of 3700 K for predators (-53%), with diffusing luminaire shapes for omnivores (-77%) and with standard luminaire shape for detritivores (-27%). The predator-detritivore ratio showed a significant color temperature - light level interaction, with increased numbers of predators around streetlights with 3700 K and 100% intensity, resulting in an elevated top-down pressure on detritivores. These results suggest the importance of considering combined light characteristics in future outdoor lighting designs.

Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impact assessment.

Environmental interactions of marine renewable energy developments vary from fine-scale direct (e.g. potential collision) to indirect wide-scale hydrodynamic changes altering oceanographic features. Current UK Environmental Impact Assessment (EIA) and associated Habitats Regulations Appraisal (HRA) guidelines have limited focus on underlying processes affecting distribution and movements (hence vulnerability) of top predators. This study integrates multi-trophic ship survey (active acoustics and observer data) with an upward-facing seabed platform and 3-dimensional hydrodynamic model as a process-driven framework to investigate predator-prey linkages between seabirds and fish schools. Observer-only data highlighted the need to measure physical drivers of variance in species abundances and distributions. Active acoustics indicated that in situ (preferable to modelled) data were needed to identify temporal changes in hydrodynamics to predict prey and consequently top predator presence. Revising methods to identify key habitats and environmental covariates within current regulatory frameworks will enable more robust and transferable EIA and HRA processes and outputs, and at larger scales for cumulative and strategic-level assessments, enabling future modelling of ecosystem impacts from both climate change and renewable energy extraction.

High temperature melting of municipal solid waste incineration (MSWI) fly ash and co-disposal technology with blast furnaces.

With the development of municipal solid waste incineration technologies, the disposal of fly ash has become a difficult problem that many countries need to solve. High-temperature melting is a promising disposal technology. Based on this, a new process for collaborative treatment of fly ash in metallurgical blast furnaces had been proposed in this study. To explore the impact of disposal of fly ash on blast furnace production, by simulating the high-temperature reducing environment of blast furnaces, the melting changes of water-washed fly ash (W-FA), and the effects of W-FA injection on coal combustion and products (slag, iron) composition were studied. The results showed that W-FA, as a flux, could be sprayed into the blast furnace separately or mixed with coal. But when injected along with coal, W-FA would suppress the combustion of coal. After melting, the removal rates of S, P, Cl, and Pb in W-FA were 21%, 30%, 86%, and 89%, respectively. The removal rates of K, Na, and Zn were close to 100%, and Cr was basically not removed. When the proportion of W-FA to coal was less than 1%, in addition to controlling the alkalinity of the slag, the impact of W-FA on the composition of iron and slag was minimal. The successful execution of this work will not only achieve the reduction, harmless and resourceful utilization of fly ash, but also save investment and operating costs of disposal facilities, with both environmental and social benefits.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.